Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.
CNS Neurosci Ther
; 30(7): e14848, 2024 Jul.
Article
in En
| MEDLINE
| ID: mdl-38973193
ABSTRACT
AIMS:
To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes.METHODS:
Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm.RESULTS:
The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use.CONCLUSION:
Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Hospital Mortality
/
Electronic Health Records
/
Machine Learning
/
Brain Injuries, Traumatic
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
CNS Neurosci Ther
Journal subject:
NEUROLOGIA
/
TERAPEUTICA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
United kingdom